Fast noise variance estimation
Computer Vision and Image Understanding
Training methods for image noise level estimation on wavelet components
EURASIP Journal on Applied Signal Processing
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Fast and reliable structure-oriented video noise estimation
IEEE Transactions on Circuits and Systems for Video Technology
Gaussian Noise Level Estimation in SVD Domain for Images
ICME '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo
Robust noise estimation based on noise injection
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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Noise estimation is an important premise for image denoising and many other image processing applications, and related research has drawn increasing attention and interest. In this paper, a novel noise level estimation algorithm is proposed by investigating the distribution of local variances in natural images. There are two major contributions of this work to tackle with the challenges in noise estimation: 1) a wavelet decomposition based preliminary estimation stage to alleviate the influence of image's textural or structural information; 2) a noise injection based estimation stage to simulate the impact of noise-free image content on the variance distribution, which otherwise almost always leads to overestimation. The cascade scheme of this two-step estimation procedure can reduce the detrimental influence of textural image regions effectively and therefore relieves overestimation of the noise variance. Moreover, the proposed method is not limited to any specific type of noise distribution. Extensive experiments and comparative analysis demonstrate that the proposed algorithm can reliably infer noise levels and has robust performance over a wide range of visual content, as compared to relevant methods.